Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks

Abstract– This paper presents a novel change detection
approach for synthetic aperture radar images based on deep
learning. The approach accomplishes the detection of the changed
and unchanged areas by designing a deep neural network. The
main guideline is to produce a change detection map directly
from two images with the trained deep neural network. The
method can omit the process of generating a difference image (DI)
that shows difference degrees between multitemporal synthetic
aperture radar images. Thus, it can avoid the effect of the
DI on the change detection results. The learning algorithm for
deep architectures includes unsupervised feature learning and
supervised fine-tuning to complete classification. The unsupervised feature learning aims at learning the representation of the
relationships between the two images. In addition, the supervised
fine-tuning aims at learning the concepts of the changed and
unchanged pixels. Experiments on real data sets and theoretical
analysis indicate the advantages, feasibility, and potential of the
proposed method. Moreover, based on the results achieved by
various traditional algorithms, respectively, deep learning can
further improve the detection performance.